Abstract

In the past decade, with the rapid development of wireless communication and sensor technology, ubiquitous smartphones equipped with increasingly rich sensors have more powerful computing and sensing abilities. Thus, mobile crowdsensing has received extensive attentions from both industry and academia. Recently, plenty of mobile crowdsensing applications come forth, such as indoor positioning, environment monitoring, and transportation. However, most existing mobile crowdsensing systems lack vast user bases and thus urgently need appropriate incentive mechanisms to attract mobile users to guarantee the service quality. In this paper, we propose to incorporate sensing platform and social network applications, which already have large user bases to build a three-layer network model. Thus, we can publicize the sensing platform promptly in large scale and provide long-term guarantee of data sources. Based on a three-layer network model, we design incentive mechanisms for both intermediaries and the crowdsensing platform and provide a solution to cope with the problem of user overlapping among intermediaries. We theoretically prove the properties of our proposed incentive mechanisms, including incentive compatibility, individual rationality, and efficiency. Furthermore, we evaluate our incentive mechanisms by extensive simulations. Evaluation results validate the effectiveness and efficiency of our proposed mechanisms.

Highlights

  • Nowadays smartphones have become indispensable elements for communication and entertainment in our daily lives

  • We model the system as a three-layer network model and design incentive mechanisms for both intermediary and sensing platform

  • All user bids are randomly generated following the normal distribution in the range of (0,2)

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Summary

Introduction

Nowadays smartphones have become indispensable elements for communication and entertainment in our daily lives. More and more embedded sensors have been integrated into smartphone system, such as GPS, gyroscope, accelerometer, digital compass, microphone, and camera [1, 2]. These sensors provide rich possibilities of smartphone functionality and bring emerging sensing paradigms, which have attracted attentions from both industry and academia. As human-involved applications, the reliability and accuracy of mobile crowdsensing systems are affected by human factors, such as dynamic joining and leaving, inaccurate or even corrupted sensing, and environment changing due to user mobility. Mobile crowdsensing systems rely on a large and active user community to guarantee adequate

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